---
type: api
title: Create a AI21 inference endpoint
description: Create an inference endpoint to perform an inference task with the `ai21` service.
url: https://www.elastic.co/elastic/docs-builder/docs/4493/api/doc/elasticsearch/v9/operation/operation-inference-put-ai21
resource: https://www.elastic.co/elastic/docs-builder/docs/4493/api/doc/elasticsearch/v9/operation/operation-inference-put-ai21
products:
  - Elasticsearch
---

# Create a AI21 inference endpoint

Availability: Generally available since 9.2

## Paths

- `PUT` `/_inference/{task_type}/{ai21_inference_id}`

#### Path parameters

- `task_type` (enum) — required
  The type of the inference task that the model will perform.
  Values: `completion`, `chat_completion`
- `ai21_inference_id` (string) — required
  The unique identifier of the inference endpoint.

## Description

Create an inference endpoint to perform an inference task with the `ai21` service.

## Required authorization

- Cluster privileges: `manage_inference`

## Authentication

- Api key: `Authorization: <value>`
  Elasticsearch APIs support key-based authentication.
  You must create an API key and use the encoded value in the request header.
  For example:
  
  
  ```
  curl -X GET "${ES_URL}/_cat/indices?v=true" \
    -H "Authorization: ApiKey ${API_KEY}"
  ```
  
  To get API keys, use the `/_security/api_key` APIs.
- Basic: `Authorization: Basic <credentials>`
- Bearer: `Authorization: Bearer <token>`
  Elasticsearch APIs support the use of bearer tokens in the `Authorization` HTTP header to authenticate with the API.
  For examples, refer to [Token-based authentication services](https://www.elastic.co/docs/deploy-manage/users-roles/cluster-or-deployment-auth/token-based-authentication-services)

## Query string parameters

- `timeout` (string · default: 30s) — optional
  Specifies the amount of time to wait for the inference endpoint to be created.

## Request body

`application/json`

- `service` (enum) — required
  The type of service supported for the specified task type. In this case, `ai21`.
  Values: `ai21`
- `service_settings` (object) — required
  Settings used to install the inference model. These settings are specific to the `ai21` service.
  - `model_id` (string) — required
    The name of the model to use for the inference task.
Refer to the AI21 models documentation for the list of supported models and versions.
Service has been tested and confirmed to be working for `completion` and `chat_completion` tasks with the following models:
* `jamba-mini`
* `jamba-large`
  - `api_key` (string) — optional
    A valid API key for accessing AI21 API.

IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.
  - `rate_limit` (object) — optional
    This setting helps to minimize the number of rate limit errors returned from the AI21 API.
By default, the `ai21` service sets the number of requests allowed per minute to 200. Please refer to AI21 documentation for more details.
    - `requests_per_minute` (number) — optional
      The number of requests allowed per minute.
By default, the number of requests allowed per minute is set by each service as follows:

* `alibabacloud-ai-search` service: `1000`
* `amazonbedrock` service: `240`
* `anthropic` service: `50`
* `azureaistudio` service: `240`
* `azureopenai` service and task type `text_embedding`: `1440`
* `azureopenai` service and task types `completion` or `chat_completion`: `120`
* `cohere` service: `10000`
* `contextualai` service: `1000`
* `elastic` service and task type `chat_completion`: `240`
* `fireworksai` service: `6000`
* `googleaistudio` service: `360`
* `googlevertexai` service: `30000`
* `hugging_face` service: `3000`
* `jinaai` service: `2000`
* `llama` service: `3000`
* `mistral` service: `240`
* `openai` service and task type `text_embedding`: `3000`
* `openai` service and task type `completion`: `500`
* `openshift_ai` service: `3000`
* `voyageai` service: `2000`
* `watsonxai` service: `120`

## Response

`application/json`

- `chunking_settings` (object)
  The chunking configuration object.
Applies only to the `embedding`, `sparse_embedding` and `text_embedding` task types.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.
  - `max_chunk_size` (number · default: 250)
    The maximum size of a chunk in words.
This value cannot be lower than `20` (for `sentence` strategy) or `10` (for `word` strategy).
This value should not exceed the window size for the associated model.
  - `overlap` (number · default: 100)
    The number of overlapping words for chunks.
It is applicable only to a `word` chunking strategy.
This value cannot be higher than half the `max_chunk_size` value.
  - `sentence_overlap` (number · default: 1)
    The number of overlapping sentences for chunks.
It is applicable only for a `sentence` chunking strategy.
It can be either `1` or `0`.
  - `separator_group` (string)
    Only applicable to the `recursive` strategy and required when using it.

Sets a predefined list of separators in the saved chunking settings based on the selected text type.
Values can be `markdown` or `plaintext`.

Using this parameter is an alternative to manually specifying a custom `separators` list.
  - `separators` (string[])
    Only applicable to the `recursive` strategy and required when using it.

A list of strings used as possible split points when chunking text.

Each string can be a plain string or a regular expression (regex) pattern.
The system tries each separator in order to split the text, starting from the first item in the list.

After splitting, it attempts to recombine smaller pieces into larger chunks that stay within
the `max_chunk_size` limit, to reduce the total number of chunks generated.
  - `strategy` (string · default: sentence)
    The chunking strategy: `sentence`, `word`, `none` or `recursive`.

 * If `strategy` is set to `recursive`, you must also specify:

- `max_chunk_size`
- either `separators` or`separator_group`

Learn more about different chunking strategies in the linked documentation.
- `service` (string)
  The service type
- `service_settings` (object)
  Settings specific to the service
- `task_settings` (object)
  Task settings specific to the service and task type
- `inference_id` (string)
  The inference Id
- `task_type` (enum)
  The task type
  Values: `completion`, `chat_completion`
## Code examples

#### Console

```console
PUT _inference/completion/ai21-completion
{
  "service": "ai21",
  "service_settings": {
    "api_key": "ai21-api-key",
    "model_id": "jamba-large" 
  }
}
```

#### JavaScript

```javascript
const response = await client.inference.put({
  task_type: "completion",
  inference_id: "ai21-completion",
  inference_config: {
    service: "ai21",
    service_settings: {
      api_key: "ai21-api-key",
      model_id: "jamba-large",
    },
  },
});
```

#### Python

```python
resp = client.inference.put(
    task_type="completion",
    inference_id="ai21-completion",
    inference_config={
        "service": "ai21",
        "service_settings": {
            "api_key": "ai21-api-key",
            "model_id": "jamba-large"
        }
    },
)
```

#### curl

```curl
curl -X PUT "$ELASTICSEARCH_URL/_inference/completion/ai21-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "ai21",
    "service_settings": {
      "api_key": "ai21-api-key",
      "model_id": "jamba-large"
    }
  }'
```

#### Java

```java
client.inference().put(p -> p
    .inferenceId("ai21-completion")
    .taskType(TaskType.Completion)
    .inferenceConfig(i -> i
        .service("ai21")
        .serviceSettings(JsonData.fromJson("""
{"api_key":"ai21-api-key","model_id":"jamba-large"}
"""))
    )
);
```

#### C#

```c#
var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "ai21-completion",
        TaskType = TaskType.Completion,
        InferenceConfig = new()
        {
            Service = "ai21",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "api_key": "ai21-api-key",
              "model_id": "jamba-large"
            }
            """)
        }
    });
```

#### PHP

```php
$resp = $client->inference()->put([
    "task_type" => "completion",
    "inference_id" => "ai21-completion",
    "body" => [
        "service" => "ai21",
        "service_settings" => [
            "api_key" => "ai21-api-key",
            "model_id" => "jamba-large",
        ],
    ],
]);
```

#### Ruby

```ruby
response = client.inference.put(
  task_type: "completion",
  inference_id: "ai21-completion",
  body: {
    "service": "ai21",
    "service_settings": {
      "api_key": "ai21-api-key",
      "model_id": "jamba-large"
    }
  }
)
```